Our model and the training routine are heavily based on JAMPR[1]}. The original JAMPR model is an extension of the attention model (AM)[2]} of Kool et al. which uses an encoder-decoder architecture involving self-attention[3]}. Both models treat the Learning-to-Optimize problem as a sequential decision making problem, that is modeled as a Markov Decision Process and solved with reinforcement learning. The solution is constructed piece by piece by creating routes one node at a time, i.e. interpreting the current solution, route and unvisited nodes as state and the index of all unvisited nodes, which can feasibly be added to the current route, as actions.
